The photographs captured by digital cameras usually suffer from over or under exposure problems. For image exposure enhancement, the tasks of Single-Exposure Correction (SEC) and Multi-Exposure Fusion (MEF) are widely studied in the image processing community. However, current SEC or MEF methods are developed under different motivations and thus ignore the internal correlation between SEC and MEF, making it difficult to process arbitrary-length sequences with improper exposures. Besides, the MEF methods usually fail at estimating the exposure of a sequence containing only under-exposed or over-exposed images. To alleviate these problems, in this paper, we develop a novel Fusion-Correction Network (FCNet) to tackle an arbitrary-length (including one) image sequence with improper exposures. This is achieved by fusing and correcting an image sequence by Laplacian Pyramid (LP) image decomposition. In each LP level, the low-frequency base component of the input image sequence is fed into a Fusion block and a Correction block sequentially for consecutive exposure estimation, implemented by alternative exposure fusion and correction. The exposure-corrected image in current LP level is upsampled and fused with the high-frequency detail components of the input image sequence in the next LP level, to output the base component for the Fusion and Correction blocks in next LP level. Experiments on the benchmark dataset demonstrate that our FCNet is effective on arbitrary-length exposure estimation, including both SEC and MEF. The code is publicly released at https://github.com/NKUJinLiang/FCNet.

翻译：数码相机拍摄的照片通常存在过曝或欠曝问题。在图像曝光增强领域，单次曝光校正（SEC）与多曝光融合（MEF）任务已在图像处理领域得到广泛研究。然而，现有SEC或MEF方法基于不同动机开发，忽略了SEC与MEF之间的内在关联，导致难以处理包含异常曝光的任意长度序列。此外，MEF方法在估计仅包含欠曝或过曝图像的序列时通常失效。为解决上述问题，本文提出一种新型融合-校正网络（FCNet），用于处理任意长度（包括单张图像）的异常曝光图像序列。该网络通过拉普拉斯金字塔（LP）图像分解实现序列的融合与校正。在每个LP层级中，输入图像序列的低频基分量依次经过融合模块与校正模块进行连续曝光估计，并通过交替曝光融合与校正实现。当前LP层级的曝光校正图像经上采样后，与下一LP层级的输入图像序列高频细节分量融合，输出作为下一LP层级融合模块与校正模块的基分量。在基准数据集上的实验表明，本FCNet在任意长度（包括SEC与MEF）的曝光估计任务中均表现有效。代码已开源发布于https://github.com/NKUJinLiang/FCNet。